Presentation 2021-03-04
Hardware Implementation of Object Recognition Neural Network using Depth Images
Yuma Yoshimoto, Hakaru Tamukoh,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) In this study, we propose an object recognition neural network using depth images, implemented on an FPGA for service robots. The proposed method achieves 4.7 points higher accuracy than Binarized VGG-16, which is one of the hardware-oriented convolutional neural networks. The network implemented on the FPGA is about 4.7 times faster than the network implemented on a CPU and about 1.9 times faster than the network implemented on a GPU. Also, the network is about 20 times more power-efficient than the network implemented on a CPU and about 8 times more power-efficient than the network implemented on a GPU.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Object Recognition / FPGA / CNN / Depth Images
Paper # SIS2020-47
Date of Issue 2021-02-25 (SIS)

Conference Information
Committee SIS
Conference Date 2021/3/4(2days)
Place (in Japanese) (See Japanese page)
Place (in English) Online
Topics (in Japanese) (See Japanese page)
Topics (in English) Soft Computing, etc.
Chair Noriaki Suetake(Yamaguchi Univ.)
Vice Chair Tomoaki Kimura(Kanagawa Inst. of Tech.) / Naoto Sasaoka(Tottori Univ.)
Secretary Tomoaki Kimura(Kindai Univ.) / Naoto Sasaoka(National Inst. of Tech., Ube College)
Assistant Yukihiro Bandoh(NTT) / Soh Yoshida(Kansai Univ.)

Paper Information
Registration To Technical Committee on Smart Info-Media Systems
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Hardware Implementation of Object Recognition Neural Network using Depth Images
Sub Title (in English)
Keyword(1) Object Recognition
Keyword(2) FPGA
Keyword(3) CNN
Keyword(4) Depth Images
1st Author's Name Yuma Yoshimoto
1st Author's Affiliation Kyushu Institute of Technology/Research Fellow of Japan Society for the Promotion of Science(Kyutech/JSPS Research Fellow)
2nd Author's Name Hakaru Tamukoh
2nd Author's Affiliation Kyushu Institute of Technology/Research Center for Neuromorphic AI Hardware(Kyutech/Research Center for Neuromorphic AI Hardware)
Date 2021-03-04
Paper # SIS2020-47
Volume (vol) vol.120
Number (no) SIS-415
Page pp.pp.67-70(SIS),
#Pages 4
Date of Issue 2021-02-25 (SIS)